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Testing Time-Aware Observation Wrapper Implementation in OpenAI Gym

This test suite validates the TimeAwareObservation wrapper functionality in OpenAI Gym environments. It ensures proper time tracking and observation space augmentation for reinforcement learning environments.

Test Coverage Overview

The test suite comprehensively covers the TimeAwareObservation wrapper functionality across multiple environments (CartPole-v1 and Pendulum-v1).

Key areas tested include:

Observation space transformation and dimensionality
Time step tracking accuracy
Reset behavior and state management
Integration with different environment types

Implementation Analysis

The testing approach uses pytest’s parametrize feature to validate the wrapper across different environment types.

Key implementation patterns include:

Parametrized testing for multiple environments
Explicit verification of observation space properties
Sequential state transition validation
Environment reset verification

Technical Details

Testing infrastructure includes:

pytest framework for test organization
OpenAI Gym environment creation and manipulation
spaces.Box for observation space handling
Environment wrapper implementation testing
Action space sampling for transition testing

Best Practices Demonstrated

The test suite exhibits several testing best practices:

Systematic state verification after actions
Comprehensive dimension checking
Clear separation of setup and assertions
Proper environment cleanup and reset verification
Parametrized testing for broader coverage

openai/gym

tests/wrappers/test_time_aware_observation.py

            
import pytest

import gym
from gym import spaces
from gym.wrappers import TimeAwareObservation


@pytest.mark.parametrize("env_id", ["CartPole-v1", "Pendulum-v1"])
def test_time_aware_observation(env_id):
    env = gym.make(env_id, disable_env_checker=True)
    wrapped_env = TimeAwareObservation(env)

    assert isinstance(env.observation_space, spaces.Box)
    assert isinstance(wrapped_env.observation_space, spaces.Box)
    assert wrapped_env.observation_space.shape[0] == env.observation_space.shape[0] + 1

    obs, info = env.reset()
    wrapped_obs, wrapped_obs_info = wrapped_env.reset()
    assert wrapped_env.t == 0.0
    assert wrapped_obs[-1] == 0.0
    assert wrapped_obs.shape[0] == obs.shape[0] + 1

    wrapped_obs, _, _, _, _ = wrapped_env.step(env.action_space.sample())
    assert wrapped_env.t == 1.0
    assert wrapped_obs[-1] == 1.0
    assert wrapped_obs.shape[0] == obs.shape[0] + 1

    wrapped_obs, _, _, _, _ = wrapped_env.step(env.action_space.sample())
    assert wrapped_env.t == 2.0
    assert wrapped_obs[-1] == 2.0
    assert wrapped_obs.shape[0] == obs.shape[0] + 1

    wrapped_obs, wrapped_obs_info = wrapped_env.reset()
    assert wrapped_env.t == 0.0
    assert wrapped_obs[-1] == 0.0
    assert wrapped_obs.shape[0] == obs.shape[0] + 1